Analyte measurment method and system

ABSTRACT

Described and illustrated herein are systems and exemplary methods of operating a multianalyte measurement system having a meter and a test strip. In one embodiment, the method may be achieved by applying a test voltage between a reference electrode and a first working electrode; measuring a first test current, a second test current and a third test current at the working electrode with the meter after a blood sample containing an analyte is applied to the test strip; estimating a hematocrit-corrected analyte concentration from the first, second and third test currents; and displaying the hematocrit-corrected analyte concentration.

CROSS-REFERENCE

This application claims the benefits of priority under 35 USC§119 and/or§120 from prior filed U.S. Provisional Application Ser. Nos. 61/246,858,filed on Sep. 29, 2009, and 61/286,106, filed Dec. 14, 2009, whichapplications are incorporated by reference in their entirety into thisapplication.

BACKGROUND

Electrochemical sensors have long been used to detect or measure thepresence of substances in fluid samples. Electrochemical sensors includea reagent mixture containing at least an electron transfer agent (alsoreferred to as an “electron mediator”) and an analyte specificbio-catalytic protein (e.g. a particular enzyme), and one or moreelectrodes. Such sensors rely on electron transfer between the electronmediator and the electrode surfaces and function by measuringelectrochemical redox reactions. When used in an electrochemicalbiosensor system or device, the electron transfer reactions aremonitored via an electrical signal that correlates to the concentrationof the analyte being measured in the fluid sample.

The use of such electrochemical sensors to detect analytes in bodilyfluids, such as blood or blood derived products, tears, urine, andsaliva, has become important, and in some cases, vital to maintain thehealth of certain individuals. In the health care field, people such asdiabetics, for example, must monitor a particular constituent withintheir bodily fluids. A number of systems are capable of testing a bodyfluid, such as, blood, urine, or saliva, to conveniently monitor thelevel of a particular fluid constituent, such as, cholesterol, proteins,and glucose. Patients suffering from diabetes, a disorder of thepancreas where insufficient insulin production prevents the properdigestion of sugar, have a need to carefully monitor their blood glucoselevels on a daily basis. Routine testing and controlling blood glucosefor people with diabetes can reduce their risk of serious damage to theeyes, nerves, and kidneys.

Electrochemical biosensors may be adversely affected by the presence ofcertain blood components that may undesirably affect the measurement andlead to inaccuracies in the detected signal. This inaccuracy may resultin an inaccurate glucose reading, leaving the patient unaware of apotentially dangerous blood sugar level, for example. As one example,the blood hematocrit level (i.e. the percentage of the amount of bloodthat is occupied by red blood cells) can erroneously affect a resultinganalyte concentration measurement.

Variations in a volume of red blood cells within blood can causevariations in glucose readings measured with disposable electrochemicaltest strips. Typically, a negative bias (i.e., lower calculated analyteconcentration) is observed at high hematocrit, while a positive bias(i.e., higher calculated analyte concentration) is observed at lowhematocrit. At high hematocrit, for example, the red blood cells mayimpede the reaction of enzymes and electrochemical mediators, reduce therate of chemistry dissolution since there is less plasma volume tosolvate the chemical reactants, and slow diffusion of the mediator.These factors can result in a lower than expected glucose reading asless current is produced during the electrochemical process. Conversely,at low hematocrit, fewer red blood cells may affect the electrochemicalreaction than expected, and a higher measured current can result. Inaddition, the blood sample resistance is also hematocrit dependent,which can affect voltage and/or current measurements.

Several strategies have been used to reduce or avoid hematocrit basedvariations on blood glucose. For example, test strips have been designedto incorporate meshes to remove red blood cells from the samples, orhave included various compounds or formulations designed to increase theviscosity of red blood cell and attenuate the affect of low hematocriton concentration determinations. Other test strips have included lysisagents and systems configured to determine hemoglobin concentration inan attempt to correct hematocrit. Further, biosensors have beenconfigured to measure hematocrit by measuring optical variations afterirradiating the blood sample with light, or measuring hematocrit basedon a function of sample chamber fill time. These methods have certaindisadvantages.

SUMMARY OF THE DISCLOSURE

Applicants have recognized a need for a system and method that can beused to determine an accurate glucose concentration that avoids thedisadvantages in the field.

In view of the foregoing and in accordance with one aspect, there isprovided a method of operating an analyte measurement system having ameter and a test strip. The test strip may include a referenceelectrode, a first working electrode and a second working electrode inwhich the first and second working electrodes are coated with a firstand second reagent layer, respectively. The respective first and secondreagent layers are disposed on a matrix layer having a mediator. Themeter may include an electronic circuit for applying a test voltagebetween the reference electrode and the first working electrode and forapplying a second test voltage between the reference electrode and thesecond working electrode. The meter also may include a signal processorfor measuring a plurality of test currents and for calculating a glucoseconcentration from the test currents. The method may be achieved byapplying a test voltage between the reference electrode and the secondworking electrode; measuring a first test current, a second test currentand a third test current at the working electrode with the meter after ablood sample containing an analyte is applied to the test strip;ascertaining the glucose concentration from the first, second and thirdtest currents; and displaying the glucose concentration.

In the exemplary method, the glucose concentration may be a valueobtained with the following:

$G = \frac{\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack - {{intercept}\; 1}}{{slope}\; 1}$

where:

-   -   G includes the hematocrit-corrected glucose concentration;    -   I₁ includes the first test current;    -   I₂ includes the second test current;    -   I₃ includes the third test current;    -   p includes a power term;    -   intercept1 includes an intercept value determined from a linear        regression of a plot of

$\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack$

versus a reference glucose concentration; and

-   -   slope1 includes a slope value determined from a linear        regression of a plot of

$\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack$

versus the reference glucose concentration.

In such embodiment, the power term p depends on a threshold value of thefirst test current I₁ and may be from about one to about four. If thefirst test current I₁ includes above the threshold value, then the aboveequation is used to calculate the hematocrit-corrected glucoseconcentration G. If the first test current I₁ is at or below thethreshold value, then the power term p is set to zero in the aboveequation and the term

$\left( \frac{I_{1}}{I_{2}} \right)^{p}$

becomes one. The threshold value of the first test current I₁ may befrom about 4 microamperes to about 7 microamperes.

In another embodiment, the power term p may include a value obtainedwith the following:

$p = {a - \frac{b}{I_{3}}}$

-   -   where a includes a first tuning parameter and b includes a        second tuning parameter.

In one embodiment, each of first and second tuning parameters a and b isfrom about zero to about five.

In another embodiment, batch-specific tuning parameters a and b may bedetermined by a calculating a first power term for a first combinationof the first tuning parameter and the second tuning parameter with thefollowing:

${p\; 1} = {a - \frac{b}{I_{3}}}$

where p1 includes the first power term;

-   ascertaining the current for each of a plurality of samples tested    with the batch of test strips with the following:

$I_{corrected} = {\left( \frac{I_{1}}{I_{2}} \right)^{p\; 1}*I_{3}}$

where I_(corrected) includes the hematocrit-corrected current;

-   computing a slope and intercept from a linear regression of a plot    of hematocrit-corrected current versus a reference plasma glucose    concentration;-   estimating a hematocrit-corrected glucose concentration for each of    the plurality of samples with the following:

$G_{corrected} = \frac{I_{corrected} - {{intercept}\; 2}}{{slope}\; 2}$

-   -   where G_(corrected) includes the hematocrit-corrected glucose        concentration, intercept2 includes an intercept value determined        from a linear regression of a plot of I_(corrected) versus a        reference glucose concentration and slope2 includes a slope        value determined from a linear regression of a plot of        I_(corrected) versus a reference glucose concentration;

-   evaluating a bias for each of the hematocrit-corrected glucose    concentrations with equations of the form:    -   Bias_(abs)=G_(corrected)−G_(reference) for G_(reference) less        than 75 mg/dL and

${Bias}_{\%} = {\frac{G_{corrected} - G_{reference}}{G_{reference}}{for}\mspace{14mu} G_{reference}\mspace{14mu} {greater}\mspace{14mu} {than}\mspace{14mu} {or}\mspace{14mu} {equal}\mspace{11mu} {to}\mspace{14mu} 75\mspace{11mu} {mg}\text{/}{dL}}$

-   -   where Bias_(abs) includes absolute bias_(%) Bias % includes        percent bias and G_(reference) includes the reference glucose        concentration;

-   estimating accuracy for the first combination of the first and    second tuning parameters with the following:

${Accuracy} = {\frac{n\; 15}{n}*100}$

-   -   where n15 includes the number of data points within a bias        criteria and n includes the total number of data points;

-   computing a hematocrit slope from a linear regression of a plot of    the bias versus the percent hematocrit;

-   establishing a standard deviation of the bias with the following:

$s = \left( {\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{1/2}$

-   -   where s includes the standard deviation, n includes the number        of samples, x_(i) includes the sample and x includes the mean of        the sample;        repeating the previous steps for all combinations of the first        and second tuning parameters; plotting an accuracy calibration        space of the accuracy calibration space for all combinations of        the first and second tuning parameters; plotting an accuracy        calibration space of the hematocrit slope calibration space for        all combinations of the first and second tuning parameters;        generating a combined surface plot for all combinations of the        first and second tuning parameters which meet an accuracy and        hematocrit slope acceptance criteria; and determining        batch-specific first and second tuning parameters from the        combined surface plot.

In another embodiment, the method of determining batch-specific tuningparameters further may include determining a set of batch-specificcalibration parameters, e.g., slope and intercept.

In yet another embodiment, the method of determining batch-specifictuning parameters further may include determining tuning parameters formultiple batches of test strips and then determining regions of overlapfor all the batches in the combined surface plots of the accuracycalibration space and the hematocrit slope calibration space.

In yet a further embodiment, a method for determining ahematocrit-corrected test current measurable with a system having a teststrip and a meter is provided. The method can be achieved by applying atest voltage between a reference electrode and a working electrodecoated with a reagent layer disposed on a matrix layer having amediator; measuring a first test current, a second test current and athird test current at the working electrode with the meter after a bloodsample containing an analyte is applied to the test strip; andascertaining a hematocrit-corrected test current via a ratio of thefirst test current to the second test current raised to a power term andmultiplying the ratio by the third test current, in which the power termis a function of a first tuning parameter and a second tuning parameter.

In yet a further embodiment, an analyte measurement system to measure atleast glucose concentration in physiological fluid of a user isprovided. The system includes a test strip and a meter. The test stripincludes a substrate having a reference electrode and a workingelectrode coated with a reagent layer, which is disposed on a matrixlayer having a mediator. The electrodes are connected to correspondingcontact pads. The analyte meter has a test circuit in connection with atest strip port that receives the contact pads of the test strip so thatthe meter is configured to apply a test voltage after deposition ofphysiological fluid on the electrodes and determine ahematocrit-corrected the glucose concentration from measured first,second and third test currents at first, second, and third discreteintervals after application of the test voltage by the meter.

These and other embodiments, features and advantages of the inventionwill become apparent to those skilled in the art when taken withreference to the following more detailed description of the exemplaryembodiments in conjunction with the accompanying drawings that are firstbriefly described.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitutepart of this specification, illustrate presently preferred embodimentsof the invention, and, together with the general description given aboveand the detailed description given below, serve to explain features ofthe invention (in which like numerals represent like elements), ofwhich:

FIG. 1 illustrates an exemplary embodiment of a top view of a system formeasuring two analyte concentrations;

FIG. 2 illustrates an exemplary embodiment of a perspective explodedview of a test strip;

FIG. 3 illustrates an exemplary embodiment of a top view of the teststrip shown in FIG. 2;

FIG. 4 illustrates an exemplary embodiment of a schematic of thefunctional components of the meter shown in FIG. 1 forming an electricalconnection with the test strip of FIGS. 2 and 3;

FIG. 5A illustrates an exemplary embodiment of a flow chart of a methodof estimating a hematocrit-corrected glucose concentration using thesystem shown in FIG. 1;

FIG. 6 illustrates an exemplary embodiment of a chart showing testvoltages applied by the meter to the test strip;

FIG. 7 illustrates an exemplary embodiment of a chart showing testcurrents generated when the test voltages of FIG. 6 are applied to thetest strip;

FIG. 8 illustrates an exemplary embodiment of a surface plot of theaccuracy calibration space for all combinations of the first tuningparameter and the second tuning parameter for a batch of test stripshaving the embodiment shown in FIGS. 2 and 3;

FIG. 9 illustrates an exemplary embodiment of a surface plot of thehematocrit slope calibration space for all combinations of the firsttuning parameter and the second tuning parameter for a batch of teststrips having the embodiment shown in FIGS. 2 and 3;

FIG. 10 illustrates an exemplary embodiment of a combined surface plotfor all combinations of the first and second tuning parameters whichmeet an accuracy and hematocrit slope acceptance criteria and using thedata in FIGS. 8 and 9;

FIGS. 11A and 11B illustrate Clarke Error Grid analysis showing testglucose concentration plotted as a function of reference glucoseconcentration prior to and after applying an exemplary embodiment to thetest data, respectively. The test data was obtained with a batch of teststrips having the embodiment shown in FIGS. 2 and 3; and

FIGS. 11C and 11D illustrate Parkes Error Grid analysis showing testglucose concentration plotted as a function of reference glucoseconcentration prior to and after applying an exemplary embodiment to thetest data, respectively. The test data in FIGS. 11A and 11B was usedalong with additional data and after applying a suitable error trapping.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The following detailed description should be read with reference to thedrawings, in which like elements in different drawings are identicallynumbered. The drawings, which are not necessarily to scale, depictselected embodiments and are not intended to limit the scope of theinvention. The detailed description illustrates by way of example, notby way of limitation, the principles of the invention. This descriptionwill clearly enable one skilled in the art to make and use theinvention, and describes several embodiments, adaptations, variations,alternatives and uses of the invention, including what is presentlybelieved to be the best mode of carrying out the invention.

As used herein, the terms “about” or “approximately” for any numericalvalues or ranges indicate a suitable dimensional tolerance that allowsthe part or collection of components to function for its intendedpurpose as described herein. In addition, as used herein, the terms“patient,” “host,” “user,” and “subject” refer to any human or animalsubject and are not intended to limit the systems or methods to humanuse, although use of the subject invention in a human patient representsa preferred embodiment.

FIG. 1 illustrates a system 100 for measuring at least two analyteconcentrations in which system 100 may include a meter 102 and a teststrip 200. Meter 102 may include a display 104, a housing 106, aplurality of user interface buttons 108, and a strip port 110. Meter 102further may include electronic circuitry within housing 106 such as amemory 120, a microprocessor 122, electronic components for applying atest voltage, and also for measuring at least two test current values. Aproximal portion 204 of test strip 200 may be inserted into strip port110. Display 104 may output at least two analyte concentrations, e.g.,glucose and/or a ketone concentration, and may be used to show a userinterface for prompting a user on how to perform a test. The pluralityof user interface buttons 108 allow a user to operate meter 102 bynavigating through the user interface software. Display 104 mayoptionally include a backlight.

An optional data port 114 accepts a suitable connector attached to aconnecting lead, thereby allowing meter 102 to be linked to an externaldevice such as a personal computer. Data port 114 may be any port thatallows for transmission of data (serial or parallel) such as, forexample, serial or parallel port in wired or wireless form. A personalcomputer, running appropriate software, allows entry and modification ofset-up information (e.g. the current time, date, and language), and mayperform analysis of data collected by meter 102. In addition, thepersonal computer may be able to perform advanced analysis functions,and/or transmit data to other computers (i.e. over the internet) forimproved diagnosis and treatment. Connecting meter 102 with a local orremote computer facilitates improved treatment by health care providers.

FIGS. 2 and 3 are exemplary exploded perspective and top assembledviews, respectively, of test strip 200, which may include seven layersdisposed on a substrate 205. The seven layers disposed on substrate 205may be a conductive layer 250, an insulation layer 216, a matrix layer222, a first reagent layer 224 and a second reagent layer 226, anadhesive layer 260, a hydrophilic layer 270, and a top layer 280. Teststrip 200 may be manufactured in a series of steps where the conductivelayer 250, insulation layer 216, matrix layer 222, first reagent layer224, second reagent layer 226 and adhesive layer 260 are sequentiallydeposited on substrate 205 using, for example, a screen-printingprocess. Hydrophilic layer 270 and top layer 280 may be disposed from aroll stock and laminated onto substrate 205 as either an integratedlaminate or as separate layers. Test strip 200 has a distal portion 203and a proximal portion 204, as shown in FIG. 2.

Test strip 200 may include a sample-receiving chamber 292 through whicha blood sample may be drawn. Sample-receiving chamber 292 may include aninlet at a proximal end of test strip 200. An outlet or air vent isincluded in hydrophilic layer 270, as will be described below. A bloodsample may be applied to the inlet to fill a sample-receiving chamber292 so that at least two analyte concentrations may be measured. Theside edges of a cut-out portion of adhesive layer 260 located adjacentto first and second reagent layers 224 and 226 define a wall ofsample-receiving chamber 292, as illustrated in FIG. 2. A bottom portionor “floor” of sample-receiving chamber 292 may include a portion ofsubstrate 205, conductive layer 250, and insulation layer 216. A topportion or “roof” of sample-receiving chamber 292 may include distalhydrophilic portion 232.

For test strip 200, as illustrated in FIG. 2, substrate 205 may be usedas a foundation for helping support subsequently applied layers.Substrate 205 may be in the form of a polyester sheet such as apolyethylene tetraphthalate (PET) material. Substrate 205 may be in aroll format, nominally 350 microns thick by 370 millimeters wide andapproximately 60 meters in length.

A conductive layer is required for forming electrodes that may be usedfor the electrochemical measurement of glucose. Conductive layer 250 maybe made from a carbon ink that is screen-printed onto substrate 205. Ina screen-printing process, carbon ink is loaded onto a screen and thentransferred through the screen using a squeegee. The printed carbon inkmay be dried using hot air at about 140° C. The carbon ink may includeVAGH resin, carbon black, graphite, and one or more solvents for theresin, carbon and graphite mixture. More particularly, the carbon inkmay incorporate a suitable ratio of carbon black:VAGH resin in thecarbon ink.

For test strip 200, as illustrated in FIG. 2, conductive layer 250 mayinclude a reference electrode 210, a first working electrode 212, asecond working electrode 214, a reference contact pad 211, a firstcontact pad 213, a second contact pad 215, a reference electrode track207, a first working electrode track 208, a second working electrodetrack 209, and a strip detection bar 217. In the embodiment shown inFIG. 2, reference electrode 210 is located in between first workingelectrode 212 and second electrode 214 such that cross-talk betweenfirst and second working electrodes 212 and 214 is minimized.

Conductive layer 250 may be formed from a carbon ink. Reference contactpad 211, first contact pad 213 and second contact pad 215 may beconfigured to electrically connect to a test meter. Reference electrodetrack 207 provides an electrically continuous pathway from referenceelectrode 210 to reference contact pad 211. Similarly, first workingelectrode track 208 provides an electrically continuous pathway fromfirst working electrode 12 to first contact pad 213. Similarly, secondworking electrode track 209 provides an electrically continuous pathwayfrom second working electrode 214 to second contact pad 215. Stripdetection bar 217 is electrically connected to reference contact pad211. A test meter may detect that test strip 200 has been properlyinserted by measuring a continuity between reference contact pad 211 andstrip detection bar 217.

Insulation layer 216 may include a rectangular aperture 218 that exposesa portion of reference electrode 210, first working electrode 212, andsecond working electrode 214, which may be wetted by a liquid sample.The area of first working electrode 212, second working electrode 214,and reference electrode 210 may be defined as the area exposed to theliquid sample. In addition to defining an electrode area, insulationlayer 216 prevents a liquid sample from touching the electrode tracks207, 208, and 209. It is believed that the functional area of a workingelectrode should be accurately defined because the magnitude of the testcurrent is directly proportional to the effective area of the electrode.As an example, insulation layer 216 may be Ercon E6110-116 Jet BlackInsulayer™ ink that may be purchased from Ercon, Inc. The test strip atthis point may be treated with plasma. The plasma is created byhigh-voltage alternating current (AC) between two or more plasma sourcesspaced about 100 millimeters apart and rotated about a generallyvertical axis at ambient temperatures to define a plasma ring. Theplasma ring is configured to be spaced apart from the substrate 205,which may include the test strip electrode, at a distance ofapproximately 5 millimeters to approximately 30 millimeters andpreferably from about 10 millimeters to about 20 millimeters. Thevoltage utilized by the plasma controller may be configured to be about5 kVA and the voltage provided to the plasma electrodes is preferablyless than about 2 kVA. The frequency of the AC is about 16 kHz to about20 kHz. The resulting ring of plasma, consisting of ionised, highlyenergetic particles is swept downstream towards the substrate 205 usingfiltered and generally contaminant free compressed air at about 1.2 barsor higher absolute pressure, preferably about 2.5 bars at a volumetricflow rate of less than 2 cubic meter of air per hour, towards thesubstrate 205, which may be moving orthogonally to the flow of air atabout 5 meters per minute to about 15 meters per minute and preferablyapproximately 10 meters per minute. The plasma ring may be arrayedadjacent to other plasma rings along the path of travel of thesubstrates. The number of plasma rings may be from one to as many asnecessary along the path of travel of the substrate or transverse tosuch path to provide for surface modification of the substrate. Theplasma is used to modify the surface of the screen printed carbon basedelectrodes. This surface modification or plasma treatment is believed toincrease the electrochemical activity of the carbon surface and increasethe surface energy of the printed layers allowing for better adhesionbetween them and subsequently printed layers. Plasma treatment is alsobelieved to improve the electrochemistry of the carbon surface makingthe reaction with the mediator more ideal.

Matrix layer 222 may include a mediator such as, for example,ferricyanide and a cofactor such as, for example, nicotinamide adeninedinucleotide (NADH). In one embodiment, matrix layer 222 may includepotassium ferricyanide, NADH, Tris-HCL buffer, hydroxyethylcellulose, DC1500 Antifoam, Cabosil TS 610, poly (vinyl pyrrolidone vinyl acetate),Triton X-100, calcium chloride and analar water.

First and second reagent layers 224 and 226 are each disposed on matrixlayer 222, as illustrated in FIG. 2. First and second reagent layers 224and 226 each may include chemicals such as an enzyme which selectivityreacts with an analyte of interest such that the analyte concentrationmay be determined. The reagent layer can include an enzyme and amediator. Exemplary enzymes suitable for use in the reagent layerinclude glucose oxidase, glucose dehydrogenase (with pyrroloquinolinequinone co-factor, “PQQ”), and glucose dehydrogenase (with flavinadenine dinucleotide co-factor, “FAD”). An exemplary mediator suitablefor use in the reagent layer includes ferricyanide, which in this caseis in the oxidized form. The reagent layer can be configured tophysically transform glucose into an enzymatic by-product and in theprocess generate an amount of reduced mediator (e.g., ferrocyanide) thatis proportional to the glucose concentration. The working electrode canthen measure a concentration of the reduced mediator in the form of acurrent. In turn, glucose meter 102 can convert the current magnitudeinto a glucose concentration.

Exemplary analytes of interest for monitoring diabetes include glucoseand ketones. In one embodiment, first reagent layer 224 may include atleast one enzyme that selectively reacts with ketones and second reagentlayer 226 may include an enzyme that selectively reacts with glucose. Inanother embodiment, first reagent layer 224 may include an enzyme thatselectively reacts with glucose and second reagent layer 226 may includeat least one enzyme that selectively reacts with ketones.

In one embodiment, the components in the reagent layer used to determinethe ketone concentration may include beta-hydroxybutyrate dehydrogenase(BHD), Tris-HCL buffer, hydroxyethylcellulose, potassium ferricyanide,DC 1500 Antifoam, Cabosil TS 610, poly(vinyl pyrrolidone vinyl acetate),Triton X-100, calcium chloride and analar water. In another embodiment,the reagent layer used to measure ketones may include a second enzymesuch as, for example, diaphorase

Examples of enzymes suitable for use in the reagent layer for measuringglucose may include either glucose oxidase or glucose dehydrogenase.More specifically, the glucose dehydrogenase may have apyrrylo-quinoline quinone (PQQ) cofactor or a flavin adeninedinucleotide (FAD) cofactor. In one embodiment, the components in thereagent layer that is used to determine the glucose concentration mayinclude glucose oxidase, Tris-HCL buffer, hydroxyethylcellulose,potassium ferricyanide, DC 1500 Antifoam, Cabosil TS 610, poly(vinylpyrrolidone vinyl acetate), Triton X-100, calcium chloride and analarwater.

First and second reagent layers 224 and 226 may be formed from a reagentink, which is disposed onto matrix layer 222 and dried. Note that thereagent ink may also be referred to as an enzyme ink or reagentformulation. A reagent ink typically contains a liquid, such as abuffer, for dispersing and/or dissolving materials used for theelectrochemical detection of an analyte such as glucose. In oneembodiment, first and second reagent layers 224 and 226 may bescreen-printed in two successive steps onto matrix layer 222. Reagentink may be loaded onto a screen until it is flooded. Next, a squeegeemay be used to transfer the reagent ink through the screen and ontomatrix layer 222. After the deposition, the reagent ink may be driedusing hot air at about 50° C.

In one embodiment, the area of first reagent layer 224 and secondreagent layer 226 is sufficiently large to cover the entire area offirst working electrode 212 and second working electrode 214,respectively. Each of first and second reagent layers 224 and 226include a width and a length that is sufficiently large to at leastaccount for the largest electrode area that may be used in test strip200. The width of first and second reagent layers 224 and 226 may beabout 2 millimeters, which is more than double a width of rectangularaperture 218.

Adhesive layer 260 may be disposed on test strip 200 after thedeposition of first and second reagent layers 224 and 226. Portions ofadhesive layer 260 may be aligned to be immediately adjacent to, touch,or partially overlap with first and second reagent layers 224 and 226.Adhesive layer 260 may include a water based acrylic copolymer pressuresensitive adhesive which is commercially available. Adhesive layer 260is disposed on a portion of insulation layer 216, conductive layer 250,and substrate 205. Adhesive layer 260 binds hydrophilic layer 270 totest strip 200.

Hydrophilic layer 270 may include a distal hydrophilic portion 232 andproximal hydrophilic portion 234, as illustrated in FIG. 2. A gap 235 isincluded between distal hydrophilic portion 232 and proximal hydrophilicportion 234. Gap 235 serves as a side vent for air as blood fillssample-receiving chamber 292. Hydrophilic layer 270 may be a polyesterhaving one hydrophilic surface such as an anti-fog coating, which iscommercially available from 3M.

The final layer to be added to test strip 200 is top layer 280, asillustrated in FIG. 2. Top layer 280 may include a clear portion 236 andopaque portion 238. Top layer 280 is disposed on and adhered tohydrophilic layer 270. Top layer 280 may be a polyester that has anadhesive coating on one side. It should be noted that the clear portion236 substantially overlaps distal hydrophilic portion 232, which allowsa user to visually confirm that sample-receiving chamber 292 may besufficiently filled. Opaque portion 238 helps the user observe a highdegree of contrast between a colored fluid such as, for example, bloodwithin sample-receiving chamber 292 and opaque portion 238.

In another embodiment, the system may include a meter and test strip formeasuring one analyte, e.g., glucose, as is described in U.S. Pat. No.5,708,247, 5,951,836, 6,241,862, and 7,112,265, each of which is fullyincorporated herein by reference.

FIG. 4 shows a simplified schematic of meter 102 interfacing with teststrip 200. Meter 102 may include a reference connector 180, a firstconnector 182 and a second connector 184, which respectively form anelectrical connection to reference contact 211, first contact 213 andsecond contact 215. The three aforementioned connectors are part ofstrip port 110. When performing a test, a first test voltage source 186may apply a test voltage V_(WE2) between second working electrode 214and reference electrode 210. As a result of test voltage V_(WE2), meter102 may then measure a test current I_(WE2) at second working electrode.In a similar manner, a second test voltage source 188 applies a testvoltage V_(WE1) between first working electrode 212 and referenceelectrode 210. As a result of test voltage V_(WE1), meter 102 may thenmeasure a test current I_(WE1). In an embodiment, test voltage V_(WE2)and second test voltage V_(WE1) may be about equal. For simplifying thedescription of the following sections, the set of instructions fordetermining a hematocrit corrected glucose concentration will bedescribed for only one working electrode and reference electrode. Itshould be apparent that the embodiments should not be limited to oneworking electrode and reference electrode, but that multiple workingelectrodes may also be utilized.

Referring to FIG. 5A, a method 300 for determining ahematocrit-corrected analyte concentration (e.g., glucose) that uses theaforementioned meter 102 and test strip 200 embodiments will now bedescribed.

In exemplary step 310, meter 102 and test strip 200 are provided. Meter102 may include electronic circuitry that can be used to apply at leastone test voltage to the test strip and to measure current flowingthrough at least second working electrode 214. Meter 102 also mayinclude a signal processor with a set of instructions for the method ofdetermining at least one analyte concentration in a fluid sample asdisclosed herein. In one embodiment, the analytes are blood glucose andketone.

FIG. 6 is an exemplary chart of a test voltage applied to test strip200. Before a fluid sample is applied to test strip 200, test meter 102is in a fluid detection mode in which a test voltage of about 400millivolts is applied between second working electrode 214 and referenceelectrode 210. In exemplary step 320, the fluid sample is applied totest strip 100 at t₀ and is allowed to react with first and secondreagent layers 224 and 226 for a reaction period t_(R). The presence ofsample in the reaction zone of test strip 200 is determined by measuringthe current flowing through second working electrode 214. The beginningof reaction period t_(R) is determined to begin when the current flowingthrough second working electrode 214 reaches a desired value, typicallyabout 0.150 nanoamperes (not shown), at which point a test voltage ofzero millivolts is applied between second working electrode 214 andreference electrode 10. Reaction period t_(R) is typically from about 2to about 4 seconds after initiation of the measuring and is moretypically about 3 seconds after initiation of the measuring, i.e., aftert₁. In exemplary step 330, after reaction period t_(R), the test voltagein the subject method is applied to test strip 200 at t_(i) for a totaltest time t_(T). In an alternative method (not shown), the reactionperiod t_(R) is omitted such that the start of the test commences assoon as sufficient current is flowing through second working electrode214.

FIG. 7 is an exemplary chart of a current transient A (i.e., themeasured electrical current response in nanoamperes as a function oftime) that is measured when the test voltage of FIG. 6 is applied totest strip 200. Test currents I_(i) obtained from current transients Aare generally indicative of the analyte concentration in the sample aswill be described in exemplary step 350 below. Referring to FIGS. 6 and7, in exemplary step 340, after the test voltage is applied betweensecond working electrode 214 and reference electrode 210 at time t₁, afirst test current I₁, a second test current I₂, and a third (or end)test current I₃ are measured at times t₂, t₃ and t_(T), respectively.The test voltage applied between second working electrode 214 andreference electrode 210 is generally from about +100 millivolts to about+600 millivolts. In one embodiment in which second working electrode 214is carbon ink and the mediator is ferricyanide, the test voltage isabout +400 millivolts. Other mediator and electrode materialcombinations will require different test voltages. The duration of firsttest voltage is generally from about 4 and 6 seconds after a reactionperiod and is typically about 5 seconds after a reaction period.Typically, time t_(i) is measured relative to time t₁. In practice, eachtest current I_(i) is the average of a set of measurements obtained overa short interval, for example, five measurements obtained at 0.01 secondintervals starting at t_(i+1), where I ranges from 1 to 3.

Referring to FIG. 5A in exemplary step 350, a hematocrit-correctedglucose concentration may be determined with the following:

$\begin{matrix}{G = \frac{\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack - {{intercept}\; 1}}{{slope}\; 1}} & (1)\end{matrix}$

where:

-   -   G is the hematocrit-corrected glucose concentration;    -   I₁ is the first test current;    -   I₂ is the second test current;    -   I₃ is the third test current;    -   p is a power term that determines the strength of the hematocrit        correction: the greater the magnitude of p, the greater the        hematocrit correction, i.e., the larger is the term

$\left( \frac{I_{1}}{I_{2}} \right)$

in Equation 1;

-   -   intercept1 is an intercept value determined from a linear        regression of a plot of

$\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack$

versus a reference glucose concentration; and

-   -   slope1 may be a slope value determined from a linear regression        of a plot of

$\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack$

versus the reference glucose concentration.

In one embodiment, first test current I₁ may be from about 3 secondsafter a reaction period to about 4 seconds after a reaction period t1,second test current I₂ may be from about 4 seconds after a reactionperiod t1 to about 5 seconds after a reaction period t1, and third testcurrent I₃ may be about 5 seconds after a reaction period t1. In oneembodiment, first test current I₁ may be measured at a time at whichsignal noise is low. For plasma treated test strip, the first testcurrent may be measured at about 3.5 seconds, the second test currentmay be measured at about 4.5 seconds and the third test current at about5 seconds. For untreated test strip, the first current may be measuredat about 4 seconds; the second test current at about 4.5 seconds; andthe third test current at about 5 seconds.

In one embodiment, power term p depends on a threshold value of firsttest current I₁ and may be from about one to about four. If first testcurrent I₁ is above the threshold value, then Equation 1 is used tocalculate the hematocrit-corrected glucose concentration G. If firsttest current I₁ is at or below the threshold value, then power term pmay be set to zero in Equation 1 and the term

$\left( \frac{I_{1}}{I_{2}} \right)^{p}$

becomes one. In one embodiment, the threshold value of first testcurrent I₁ may be from about 4 microamperes to about 7 microamperes.

In another embodiment, power term p comprises a value obtained with thefollowing:

$\begin{matrix}{p = {a - \frac{b}{I_{3}}}} & (2)\end{matrix}$

where a is a first tuning parameter and b is a second tuning parameter.

By subtracting the inverse of I₃ from first tuning parameter a, powerterm p is increased for large values of I₃ and is reduced for low valuesof I₃, corresponding to high and low glucose concentrations,respectively. In one embodiment, each of first and second tuningparameters a and b is from about zero to about five. For low glucosevalues, e.g., less than about 75 mg/dL, the value of p is preferablyabout 1 while for other glucose values, the value of p can be from about1.5 to about 3.5. In exemplary step 340, the hematocrit-correctedglucose concentration may then be displayed on meter 102.

Referring to FIG. 5B, a method 400 for determining batch-specific firstand second tuning parameters a and b will now be described. In exemplarystep 410, a plurality of combinations of first and second tuningparameters a and b are provided. In an embodiment in which each of thefirst and second tuning parameters may vary from about zero to aboutfive in increments of 0.1, a total of 2601 tuning parameter combinationsare possible. In exemplary step 420, a first power term p1 for a firstcombination of the first tuning parameter and the second tuningparameter may be determined with Equation 3.

In exemplary step 430, a hematocrit-corrected current for each of aplurality of samples tested with the batch of test strips may bedetermined with the following:

$\begin{matrix}{I_{corrected} = {\left( \frac{I_{1}}{I_{2}} \right)^{p\; 1}*I_{3}}} & (3)\end{matrix}$

where I_(corrected) is a hematocrit-corrected current and p1 is thefirst power term.

In exemplary step 440, a slope2 and an intercept2 is determined from alinear regression of a plot of hematocrit-corrected current versus areference plasma glucose concentration.

In exemplary step 450, a hematocrit-corrected glucose concentration isdetermined for each of the plurality of samples with the following:

$\begin{matrix}{G_{corrected} = \frac{I_{corrected} - {{intercept}\; 2}}{{slope}\; 2}} & (4)\end{matrix}$

where:

-   -   G_(corrected) is a hematocrit-corrected glucose concentration;    -   intercept2 is the intercept value determined from a linear        regression of a plot of I_(corrected) versus a reference glucose        concentration G_(reference), and    -   slope2 is the slope value determined from a linear regression of        a plot of I_(corrected) versus a reference glucose        concentration;

In exemplary step 460, a bias for each of the hematocrit-correctedglucose concentrations is determined with equations of the form:

$\begin{matrix}{{Bias}_{abs} = {G_{corrected} - {G_{reference}\mspace{14mu} {for}\mspace{14mu} G_{reference}\mspace{14mu} {less}\mspace{14mu} {than}\mspace{14mu} 75\mspace{11mu} {mg}\text{/}{dL}\mspace{14mu} {and}}}} & (5) \\{{Bias}_{\%} = {{\frac{\left( {G_{corrected} - G_{reference}} \right)}{G_{reference}}{for}\mspace{14mu} G_{reference}}\; \geq {{to}\mspace{14mu} 75\mspace{11mu} {mg}\text{/}{dL}}}} & (6)\end{matrix}$

where:

Bias_(abs) is an absolute bias;

Bias_(%) is a percent bias;

G_(corrected) is defined above for Equation 4; and

G_(reference) is the reference glucose concentration;

In exemplary step 470, an accuracy for the first combination of thefirst and second tuning parameters is determined with the following:

$\begin{matrix}{{Accuracy} = {\frac{n\; 15}{n}*100}} & (7)\end{matrix}$

where:

n15 is the number of data points within a bias criteria; and

n is the total number of data points;

In exemplary step 480, a hematocrit slope is determined from a linearregression of a plot of the bias versus the percent hematocrit.

In exemplary step 490, a standard deviation of the bias (which may be amean bias) is determined with the following:

$\begin{matrix}{s = \left( {\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{1/2}} & (8)\end{matrix}$

where:

s is the standard deviation;

n is the number of samples;

x_(i) is the sample; and

x is the mean of the sample.

The standard deviation of the bias (which may be a mean bias) is ameasure of the noise introduced by the set of instructions.

In exemplary step 500, the previous steps for all combinations of thefirst and second tuning parameters are repeated. In exemplary step 510,a surface plot 800 (FIG. 8) of the accuracy calibration space for allcombinations of first tuning parameter a and second tuning parameter bis generated. A region 802 of acceptable accuracy may be determined fromthe accuracy calibration space. The region 802 indicates an area ofgreatest accuracy, approximately ±15% or about 12 mg/dL for accuracyrequirement. The data generated by plot 800 is calculated from a batchof plasma treated carbon type test strip. In one embodiment, a minimumaccuracy of 95% is used as an acceptance criterion.

In exemplary step 520, a surface plot 900 (FIG. 9) of the hematocritslope calibration space for all combinations of first tuning parameter aand second tuning parameter b is determined. A maximum negativehematocrit slope may then be determined from the hematocrit slopecalibration space. In one embodiment, the hematocrit slope acceptancecriterion is greater than −0.6% bias per % hematocrit, which isindicated by region 902 in plot 900.

In exemplary step 530, a combined surface plot 1000 (FIG. 10) of boththe accuracy calibration space and the hematocrit slope calibrationspace for all combinations of first tuning parameter a and second tuningparameter b is determined.

In exemplary step 540, the batch-specific first tuning parameter andsecond tuning parameter is determined from the region in the combinedsurface plot in which the acceptance criteria for both accuracy andhematocrit slope are met. In one embodiment, the acceptance criterionfor accuracy is greater than 95% and the hematocrit slope acceptancecriterion is greater than −0.5% bias per % hematocrit. Thebatch-specific first and second tuning parameters may then be used todetermine a set of batch-specific calibration parameters, e.g., slopeand intercept, by repeating steps 420, 430 and 440 in method 400. To usethe same set of tuning parameters for multiple batches of test strips, aset of tuning parameters may be determined for each batch by method 400and then regions of overlap in the combined accuracy and hematocritcalibration space for all the batches may be determined. That is,combinations which pass suitable criteria (e.g., with accuracy isgreater than 95% and the slope greater than −0.6% bias per % hct) inFIGS. 8 and 9 are retained. The resulting calibration space isillustrated by the elevated region in FIG. 10.

Example Determination of Hematocrit-Corrected Glucose Concentration witha Test Strip as Shown in FIGS. 2 and 3

A batch of test strips was tested with 432 whole blood samples having atleast three different glucose concentrations (i.e., 55 mg/dL, 240 mg/dLand 450 mg/dL) and hematocrit levels ranging from 30 to 55%. Thehematocrit-corrected glucose concentration was determined for each datapoint in the data mapping as described previously with methods 300 and400. A surface plot 800 of the accuracy calibration space for allcombinations of tuning parameters a and b was determined and isillustrated in FIG. 8. The elevated region 802 in the center of thesurface plot indicates the area of acceptable accuracy, e.g., greaterthan 95% of the values within an International Standards Organization(ISO) bias requirement of about +/−15% for glucose values greater thanor equal to about 75 mg/dL or about 12 mg/dL for glucose values lessthan about 75 mg/dL.

A surface plot 900 of the hematocrit slope calibration space for allcombinations of tuning parameters a and b was also determined and isshown in FIG. 9 for glucose concentration greater than about 100 mg/dLand less than about 300 mg/dL because it is believed that this range isthe most resistant to hematocrit correction. The region 902 in thecenter of the plot meets the acceptance criteria for the hematocritslope of greater than about −0.6% bias per % hematocrit.

FIGS. 8 and 9 illustrate a large calibration space that characterizesthe effect of all 2061 possible combinations of the tuning parameters onaccuracy and hematocrit slope. Visualizing the data in this mannerprovides a method for reducing this large calibration space into auseful set of tuning parameters. FIG. 8 suggests where there is a region(e.g., 802) of accuracy within the acceptance criteria. This region 802may be reduced further by considering the hematocrit slope along withthe accuracy. This may be achieved by setting acceptance criteria forboth the accuracy and hematocrit slope at each combination of tuningparameters. Using an accuracy requirement of greater than 95% of thedata within the ISO bias limits of +/−15% for glucose values greaterthan or equal to 75 mg/dL or 12 mg/dL for glucose values less than 75mg/dL (FIG. 8) and a hematocrit requirement of greater than −0.6% biasper % hematocrit (FIG. 9), a calibration space 1000 may determined, asillustrated by the shaded region in FIG. 10. The calibration space canbe reduced by using more narrow acceptance criteria, e.g., by increasingthe required accuracy and by reducing the allowed hematocrit slope whichresults in a smaller set of batch-specific tuning parameters.

Once the preferred set of tuning parameters a and b are obtained fromthe data mapping, they can be applied to the data set and the above isrepeated to determine the slopes and intercepts for the hematocritcompensated currents and the reference glucose values. The tuning andcalibration parameters are now set for this batch. When dealing withmultiple batches, all of the steps should be repeated for eachindividual batch, and areas in the calibration space which allow thesame set of tuning parameters to be used should be found (e.g. bycreating FIG. 10 for each batch and looking for areas of overlap).

FIGS. 11A and 11B illustrate Clarke Error Grid plots of test glucoseconcentration as a function of reference glucose concentration asdetermined on a reference instrument. A Clark's Error Grid analysisprovides a method to access the clinical accuracy of a blood glucosemonitoring device. The error grid of such an analysis categorizes adevice's response against a reference value into one of five clinicalaccuracy zones (i.e., zones A-E). Zone A indicates clinically accurateresults; zone B indicates results that are not clinically accurate butpose minimal risk to patient health; and zones C through E indicateclinically inaccurate results that pose increasing potential risk topatient health (see Clarke, William L. et al., Evaluating ClinicalAccuracy of Systems for Self Monitoring of Blood Glucose, Diabetes Care,Vol. 10 No. 5, 622-628 [1987], which is incorporated by reference as ifset forth herein). Specifications can be developed based on the percentof results falling within the various error grid zones. In the currentexample, it is desirable that at least 95% of the data lie within zone Aand the rest of the data lie within zone B. FIG. 11A illustratesuncorrected data from the given batch of test strips tested with 432whole blood samples. FIG. 11B illustrates the same set of data but withthe hematocrit-correction of the subject method applied to the datadescribed previously in methods 300 and 400. A summary of the percent ofdata falling within each zone is given in Table 1 below for uncorrecteddata and corrected data.

TABLE 1 Summary of Clarke Error Grid Analysis Percent within ZonePercent within Zone Zone for Uncorrected Data for Corrected Data A 92.298.6 B 6.7 1.2 C 0.1 0.1 D 0.9 0.0 E 0.0 0.0

The data in Table 1 illustrates an increase in the percent of datapoints in Zone A when the subject method is used to correct the data forthe hematocrit effect.

The data may also be presented as a percent falling within different ISObias criteria, as illustrated in Table 2 below. Steps 410-470 of method400 were used to determine the percent within each bias criteria.

TABLE 2 Summary of Bias Results Percent within Percent within ISO BiasCriteria Bias Criteria for Bias Criteria for (%) Uncorrected DataCorrected Data +/− 20 92.3 98.6 +/− 15 83.7 97.1 +/− 10 66.3 85.4

The data in Table 2 indicates an increase in the percent of data fallingwithin each ISO bias criteria when the subject method is used to correctthe data for the hematocrit effect.

FIGS. 11C and 11D illustrate Parkes Error Grid plots of the same data asshown in FIGS. 11A and 11B with error trapping to remove outliers. TheParkes Error Grid is a successor to the Clarke Error Grid and differsfrom the latter (a) in representing a consensus of a larger number ofphysicians and (b) in changing risk boundaries based on advances inknowledge acquired since the original publication of Clarke, et al. (seeParkes, Joan L. et al., A New Consensus Error Grid to Evaluate theClinical Significance of Inaccuracies in the Measurement of BloodGlucose, Diabetes Care, Vol. 23 No. 8, 1143-1147 [2000]). The ParkesError Grid eliminates the discontinuities of risk levels (i.e., skippingrisk categories in crossing from one zone boundary to another) of theClarke Error Grid.

FIG. 11C illustrates uncorrected data from the given batch of teststrips tested with 761 whole blood samples and with outliers removed byerror trapping. FIG. 11D illustrates the same set of data as in FIG. 11Cbut with the hematocrit-correction of the subject method applied to thedata described previously in methods 300 and 400. It is desirable thatat least 95% of the data lie within zone A and the rest of the data liewithin zone B. A summary of the percent of data falling within each zoneis given in Table 3 below for uncorrected data and corrected data.

TABLE 3 Summary of Parkes Error Grid Analysis Percent within ZonePercent within Zone Zone for Uncorrected Data for Corrected Data A 96.899.2 B 3.2 0.8 C 0.0 0.0 D 0.0 0.0 E 0.0 0.0

The data in Table 3 illustrates an increase in the percent of datapoints in Zone A when the subject method is used to correct the data forthe hematocrit effect.

In conclusion, the system and methods described and illustrated hereincan be used to determine a hematocrit-corrected glucose concentration.Thus, the glucose result obtained with the exemplary subject system andmethod is believed to be more accurate.

While the invention has been described in terms of particular variationsand illustrative figures, those of ordinary skill in the art willrecognize that the invention is not limited to the variations or figuresdescribed. In addition, where methods and steps described above indicatecertain events occurring in certain order, those of ordinary skill inthe art will recognize that the ordering of certain steps may bemodified and that such modifications are in accordance with thevariations of the invention. Additionally, certain of the steps may beperformed concurrently in a parallel process when possible, as well asperformed sequentially as described above. Therefore, to the extentthere are variations of the invention, which are within the spirit ofthe disclosure or equivalent to the inventions found in the claims, itis the intent that this patent will cover those variations as well.

1. A method for determining a glucose concentration measurable with asystem having a test strip and a meter, the method comprising: applyinga test voltage between a reference electrode and a working electrodecoated with a reagent layer disposed on a matrix layer having amediator; measuring a first test current, a second test current and athird test current at the working electrode with the meter after a bloodsample containing an analyte is applied to the test strip to physicallytransform the analyte into an enzymatic by-product; determining aglucose concentration from the first, second and third test currents;and displaying the glucose concentration.
 2. The method of claim 1, inwhich the first test current comprises a current measured from aboutthree to about four seconds after a reaction period of time.
 3. Themethod of claim 1, in which the second current comprises a currentmeasured from about four to about five seconds after a reaction periodof time.
 4. The method of claim 1, in which the third current comprisesa current at about five seconds after a reaction period of time.
 5. Themethod of claim 1, in which the glucose concentration comprises a valueobtained with the following:$G = \frac{\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack - {{intercept}\; 1}}{{slope}\; 1}$where: G comprises the glucose concentration; I₁ comprises the firsttest current; I₂ comprises the second test current; I₃ comprises thethird test current; p comprises a power term that depends on a thresholdvalue of the first test current; intercept1 comprises an intercept valuedetermined from a linear regression of a plot of$\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack$versus a reference glucose concentration for a batch of test strips; andslope1 comprises a slope value determined from a linear regression of aplot of$\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack$versus the reference glucose concentration for the particular batch oftest strip.
 6. The method of claim 5, in which the threshold value ofthe first test current comprises from about 5 microamperes to about 7microamperes.
 7. The method of claim 5, in which the power termcomprises a value obtained with the following:$p = {a - \frac{b}{I_{3}}}$ where a comprises a first tuning parameterand b comprises a second tuning parameter.
 8. The method of claim 5, inwhich the power term comprises a value from about one to about four. 9.The method of claim 7, in which batch-specific first and second tuningparameters by a method comprising: calculating a first power term for afirst combination of the first tuning parameter and the second tuningparameter with the following: ${p\; 1} = {a - \frac{b}{I_{3}}}$ wherep1 comprises the first power term; ascertaining the hematocrit-correctedcurrent for each of a plurality of samples tested with the batch of teststrips with the following:${I_{{corrected}\; =}\left( \frac{I_{1}}{I_{2}} \right)}^{p\; 1}*I_{3}$where I_(corrected) comprises the hematocrit-corrected current;computing a slope and intercept from a linear regression of a plot ofhematocrit-corrected current versus a reference plasma glucoseconcentration; estimating a hematocrit-corrected glucose concentrationfor each of the plurality of samples with the following:$G_{corrected} = \frac{I_{corrected} - {{intercept}\; 2}}{{slope}\; 2}$where: G_(corrected) comprises the hematocrit-corrected glucoseconcentration; intercept2 comprises an intercept value determined from alinear regression of a plot of I_(corrected) versus a reference glucoseconcentration; and slope2 comprises a slope value determined from alinear regression of a plot of I_(corrected) versus a reference glucoseconcentration; evaluating a bias for each of the hematocrit-correctedglucose concentrations with equations of the form:Bias_(abs)=G_(corrected)−G_(reference) for G_(reference) less than 75mg/dL and${Bias}_{\%} = {\frac{G_{corrected} - G_{reference}}{G_{reference}}{for}\mspace{14mu} G_{reference}\mspace{20mu} {greater}\mspace{14mu} {than}\mspace{14mu} {or}\mspace{14mu} {equal}\mspace{14mu} {to}\mspace{14mu} 75\mspace{11mu} {mg}\text{/}{dL}}$where: Bias_(abs) comprises absolute bias; Bias_(%) comprises percentbias; G_(corrected) is defined above; and G_(reference) is the referenceglucose concentration; estimating accuracy for the first combination ofthe first and second tuning parameters with the following:${Accuracy} = {\frac{n\; 15}{n}*100}$ where n15 comprises the numberof data points within a bias criteria; and n comprises the total numberof data points; computing a hematocrit slope from a linear regression ofa plot of the bias versus the percent hematocrit; establishing astandard deviation of the bias with the following:$s = \left( {\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}\; \left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{1/2}$where: s comprises the standard deviation; n comprises the number ofsamples; x_(i) comprises the sample; and x comprises the mean of thesample; repeating the previous steps for all combinations of the firstand second tuning parameters; plotting an accuracy calibration space ofthe accuracy calibration space for all combinations of the first andsecond tuning parameters; plotting an accuracy calibration space of thehematocrit slope calibration space for all combinations of the first andsecond tuning parameters; generating a combined surface plot for allcombinations of the first and second tuning parameters which meet bothaccuracy and hematocrit slope acceptance criteria; and determiningbatch-specific first and second tuning parameters from the combinedsurface plot.
 10. A method for determining at least a glucoseconcentration measurable with a system having a test strip and a meter,the method comprising: applying a test voltage between a referenceelectrode and a working electrode coated with a reagent layer disposedon a matrix layer having a mediator; measuring a first test current, asecond test current and a third test current at the working electrodewith the meter after a blood sample containing an analyte is applied tothe test strip; determining the glucose concentration from the first,second and third test currents with the following:$G = \frac{\left\lbrack {\left( \frac{I_{1}}{I_{2}} \right)^{p}I_{3}} \right\rbrack - {{intercept}\; 1}}{{slope}\; 1}$where: G comprises the glucose concentration; I₁ comprises the firsttest current; I₂ comprises the second test current; I₃ comprises thethird test current; Intercept1 and slope1 are data previously obtainedfrom linear regression analysis of a plot of hematocrit-correctedcurrent versus a reference plasma glucose concentration for a particularbatch of strip; p comprises a power term and comprises a value obtainedwith the following: $p = {a - \frac{b}{I_{3}}}$ where a comprises afirst tuning parameter and b comprises a second tuning parameter and thetuning parameters by for a batch of test strips; and displaying theglucose concentration.
 11. The method of claim 10, in which thedetermining comprises: calculating a first power term for a firstcombination of the first tuning parameter and the second tuningparameter with the following: ${p\; 1} = {a - \frac{b}{I_{3}}}$ wherep1 comprises the first power term; ascertaining the hematocrit-correctedcurrent for each of a plurality of samples tested with the batch of teststrips with the following:$I_{corrected} = {\left( \frac{I_{1}}{I_{2}} \right)^{p\; 1}*I_{3}}$where I_(corrected) comprises the hematocrit-corrected current;computing a slope and intercept from a linear regression of a plot ofhematocrit-corrected current versus a reference plasma glucoseconcentration; estimating a hematocrit-corrected glucose concentrationfor each of the plurality of samples; evaluating a bias for each of thehematocrit-corrected glucose concentrations; estimating an accuracy forthe first combination of the first and second tuning parameterscomputing a hematocrit slope from a linear regression of a plot of thebias versus the percent hematocrit; establishing a standard deviation ofthe bias; repeating the previous steps for all combinations of the firstand second tuning parameters; plotting an accuracy calibration space ofthe accuracy calibration space for all combinations of the first andsecond tuning parameters; plotting an accuracy calibration space of thehematocrit slope calibration space for all combinations of the first andsecond tuning parameters; generating a combined surface plot for allcombinations of the first and second tuning parameters which meet bothaccuracy and hematocrit slope acceptance criteria; and determiningbatch-specific first and second tuning parameters from the combinedsurface plot.
 12. The method of claim 11, in which the estimatingcomprises calculating with the following:$G_{corrected} = \frac{I_{corrected} - {{intercept}\; 2}}{{slope}\; 2}$where: G_(corrected) comprises the hematocrit-corrected glucoseconcentration; intercept2 comprises an intercept value determined from alinear regression of a plot of I_(corrected) versus a reference glucoseconcentration for a batch of test strips; and slope2 comprises a slopevalue determined from a linear regression of a plot of I_(corrected)versus a reference glucose concentration for the particular batch oftest strips.
 13. The method of claim 12, in which the evaluating of thebias for each of the hematocrit-corrected glucose concentrationscomprises the following: Bias_(abs)=G_(corrected)−G_(reference) forG_(reference) less than 75 mg/dL and${Bias}_{\%} = {\frac{G_{corrected} - G_{reference}}{G_{reference}}\mspace{14mu} {for}\mspace{14mu} G_{reference}\mspace{14mu} {greater}\mspace{14mu} {than}\mspace{14mu} {or}\mspace{14mu} {equal}\mspace{14mu} {to}\mspace{14mu} 75\mspace{11mu} {mg}\text{/}{dL}}$where: Bias_(abs) comprises absolute bias; Bias_(%) comprises percentbias; and G_(reference) comprises the reference glucose concentration.14. The method of claim 13, in which the estimating of the accuracy forthe first combination of the first and second tuning parameterscomprises the following: ${Accuracy} = {\frac{n\; 15}{n}*100}$ wheren15 comprises the number of data points within a bias criteria; and ncomprises the total number of data points.
 15. The method of claim 14,in which the establishing of the standard deviation of the biascomprises the following:$s = \left( {\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}\; \left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{1/2}$where: s comprises the standard deviation; n comprises the number ofsamples; x_(i) comprises the sample; and x comprises the mean of thesample.
 16. The method of claim 10, in which the first test currentcomprises a current measured from about three to about four secondsafter initiation of the measuring.
 17. The method of claim 10, in whichthe second current comprises a current measured from about four to aboutfive seconds after initiation of the measuring.
 18. The method of claim10, in which the third current comprises a current at about five secondsafter initiation of the measuring.
 19. The method of claim 10, in whichthe power term comprises a value from about one to about four.
 20. Amethod for determining a hematocrit-corrected test current measurablewith a system having a test strip and a meter, the method comprising:applying a test voltage between a reference electrode and a workingelectrode coated with a reagent layer disposed on a matrix layer havinga mediator; measuring a first test current, a second test current and athird test current at the working electrode with the meter after a bloodsample containing an analyte is applied to the test strip; andascertaining a hematocrit-corrected test current via a ratio of thefirst test current to the second test current raised to a power term andmultiplying the ratio by the third test current, in which the power termcomprises a function of a first tuning parameter and a second tuningparameter.
 21. The method of claim 20, further comprising evaluatingbatch-specific first and second tuning parameters by calculating a firstpower term for a first combination of the first tuning parameter and thesecond tuning parameter with the following:${p\; 1} = {a - \frac{b}{I_{3}}}$ where: p comprises the first powerterm I₃ comprises the third test current; and a and b are the first andsecond tuning parameters, respectively.
 22. The method of claim 21, inwhich the ascertaining of the hematocrit-corrected current for each of aplurality of samples tested with the batch of test strips comprises:$I_{corrected} = {\left( \frac{I_{1}}{I_{2}} \right)^{p\; 1}*I_{3}}$where: I_(corrected) comprises the hematocrit-corrected current; I₁comprises the first test current; and I₂ comprises the second testcurrent;
 23. The method of claim 22, further comprising: computing aslope and intercept from a linear regression of a plot ofhematocrit-corrected current versus a reference plasma glucoseconcentration for a batch of test strip; estimating ahematocrit-corrected glucose concentration for each of the plurality ofsamples with the following:$G_{corrected} = \frac{I_{corrected} - {{intercept}\; 2}}{{slope}\; 2}$where: G_(corrected) comprises the hematocrit-corrected glucoseconcentration; intercept2 comprises an intercept value determined from alinear regression of a plot of I_(corrected) versus a reference glucoseconcentration; and slope2 comprises a slope value determined from alinear regression of a plot of I_(corrected) versus a reference glucoseconcentration; evaluating a bias for each of the hematocrit-correctedglucose concentrations with the following:Bias_(abs)=G_(corrected)−G_(reference) for G_(reference) less than 75mg/dL and${Bias}_{\%} = {\frac{G_{corrected} - G_{reference}}{G_{reference}}\mspace{14mu} {for}\mspace{14mu} G_{reference}\mspace{14mu} {greater}\mspace{14mu} {than}\mspace{14mu} {or}\mspace{14mu} {equal}\mspace{14mu} {to}\mspace{14mu} 75\mspace{11mu} {mg}\text{/}{dL}}$where: Bias_(abs) comprises absolute bias; Bias_(%) comprises percentbias; and G_(reference) comprises the reference glucose concentration;estimating an accuracy for the first combination of the first and secondtuning parameters with the following:${Accuracy} = {\frac{n\; 15}{n}*100}$ where n15 comprises the numberof data points within a bias criteria; and n comprises the total numberof data points; computing a hematocrit slope from a linear regression ofa plot of the bias versus the percent hematocrit; establishing astandard deviation of the bias with the following:$s = \left( {\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}\; \left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{1/2}$where: s comprises the standard deviation; n comprises the number ofsamples; x_(s) comprises the sample; and x comprises the mean of thesample; repeating the previous steps for all combinations of the firstand second tuning parameters; plotting an accuracy calibration space ofthe accuracy calibration space for all combinations of the first andsecond tuning parameters; plotting an accuracy calibration space of thehematocrit slope calibration space for all combinations of the first andsecond tuning parameters; generating a combined surface plot for allcombinations of the first and second tuning parameters which meet bothaccuracy and hematocrit slope acceptance criteria; and determiningbatch-specific first and second tuning parameters from the combinedsurface plot.
 24. The method of claim 23, further comprising determiningcalibration values with the batch-specific first and second tuningparameters.
 25. The method of claim 20, in which the power termcomprises a value from about one to about four.
 26. The method of claim20, in which the first test current comprises a current measured fromabout three to about four seconds after initiation of the measuring. 27.The method of claim 20, in which the second current comprises a currentmeasured from about four to about five seconds after initiation of themeasuring.
 28. The method of claim 20, in which the third currentcomprises a current at about five seconds after initiation of themeasuring.
 29. An analyte measurement system to measure at least glucoseconcentration in physiological fluid of a user, the system comprising: atest strip including a substrate having a reference electrode and aworking electrode coated with a reagent layer disposed on a matrix layerhaving a mediator, the electrodes being connected to correspondingcontact pads; and an analyte meter having a test circuit in connectionwith a test strip port that receives the contact pads of the test stripso that the meter is configured to apply a test voltage after depositionof physiological fluid on the electrodes and determine ahematocrit-corrected the glucose concentration from measured first,second and third test currents at first, second, and third discreteintervals after application of the test voltage by the meter.
 30. Thesystem of claim 29, in which the first test current comprises a currentmeasured from about three to about four seconds after initiation of themeasuring.
 31. The system of claim 29, in which the second currentcomprises a current measured from about four to about five seconds afterinitiation of the measuring.
 32. The system of claim 29, in which thethird current comprises a current at about five seconds after initiationof the measuring.
 33. The system of claim 29, in which no test voltageis applied for a period of time after the deposition of thephysiological fluid to provide for a reaction time before application ofthe test voltage.